An Improved Software Adaptive Testing Method

Abstract

The invention relates to an improved software self-adaptive testing method. The method is based on a software cybernetics model, and improves the traditional self-adaptive random testing method. The tested input domain information is used as test historical data, and the adjustment algorithm is used as a parameter estimation module. The selection of partition and input domain is used as a test decision, and the number of test cases in the input domain is estimated as the input domain test step size design, and the purpose of improving test efficiency is achieved by rationally allocating test resources. According to the key problems faced by software testing, the invention can effectively solve the problem of software input space combination explosion by improving the assumptions of the traditional software self-adaptive testing method, fully utilize the intermediate testing data generated in the testing process, and realize the dynamic, dynamic, and dynamic performance of the software testing process. Adaptive feedback adjustment can effectively reduce the number of test cases, improve test efficiency, and reduce test costs. Therefore, the present invention has important engineering application value.

Description

technical field [0001] The invention belongs to the technical field of software engineering and software testing, in particular to an improved software self-adaptive testing method. Background technique [0002] With the rapid development of information technology and the increasing complexity and scale of software systems, software testing, as an important part of the life cycle, plays an increasingly important role. However, engineering practice shows that the difficulty and cost of software testing become increasingly uncontrollable as the complexity and scale of software continue to rise. Traditional software testing methods are obviously insufficient in testing efficiency and fault location, and can no longer meet the high standards and requirements of modern software testing. [0003] The traditional adaptive software testing is based on the software cybernetics model, including the software under test (the controlled object), the test strategy (the controller) and th...

AI Technical Summary

Problems solved by technology

[0004] 1) The model is difficult to fundamentally solve the problem of geometric increase in test time overhead

[0005] 2) Algorithm efficiency problem in the case of multi-dimensional, in the case of more than three dimensions, the computational efficiency of adaptive software testing is not good, and consumes a lot of testing overhead

The time complexity of adaptive software testing in screening a test case reaches O(n2), and the overhead of the algorithm for screening test cases affects the operating efficiency of the algorithm

[0006] 3) Under the condition of non-numerical input fields, there are great difficulties in the representation and calculation of the distance between use cases

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